Data Engineer

Data Engineer Data Product Design & Delivery • Design and deliver end-to-end data products for assigned business domains across the bronze, silver, and gold layers, including the dimensional and semantic models that serve reporting and AI. • Build robust ingestion from Amana's enterprise systems Oracle Fusion (Finance, HCM, P2P, CRM), Hyperion, CMiC, project and operations systems, SharePoint, and Power Platform using change data capture, incremental, and API-based patterns. • Lead workstreams within the Synapse Analytics to Databricks migration: re-engineering pipelines, running parallel loads, reconciling results, and preparing for cutover. • Contribute reusable frameworks, templates, and patterns, and propose improvements to the standards owned by the Data Engineering Lead (Data Architect). Data Quality, Governance & Security • Implement data quality frameworks, monitoring, and reconciliation across your domains, and drive issues to resolution at source with data owners. • Implement access control, lineage, classification, and data-handling rules in Unity Catalog and Microsoft Purview, including PDPL controls for personal and HR data. • Partner with the Data Governance Office to turn domain policies into working controls and documented, certified datasets. Performance, Reliability & Cost • Tune Spark and SQL workloads for performance and cost within the platform's cluster and workload policies. • Own the operational health of your domains: monitoring, alerting, incident response, and root-cause fixes. • Apply CI/CD, automated testing, and DataOps practices using Git and Azure DevOps. Analytics & AI Enablement • Build AI-ready data products feature tables and document and vector pipelines for machine learning, RAG, and agent use cases, in partnership with the Data Scientist and AI Engineer. • Deliver Power BI semantic models and certified datasets with the Data Analyst, supporting self-service adoption through the Amana Digital Academy. • Apply AI-assisted engineering code generation, testing, documentation, and lineage discovery with appropriate human review, and help the team adopt it safely. Collaboration & Mentoring • Translate requirements from finance, project, and operations stakeholders into technical designs with clear delivery outcomes. • Mentor Data Engineers through pairing, design reviews, and code review. • Explain design choices, trade-offs, and risks clearly to technical and business audiences. Required Qualifications • 5+ years of hands-on data engineering experience, including at least 2 years with Databricks, Apache Spark, and Delta Lake in production. • Designed and delivered end-to-end data products from ingestion to modelled, consumable data for one or more business domains. • Experience integrating complex enterprise sources such as Oracle Fusion, SAP, Microsoft Dynamics, or CMiC, including incremental and API-based extraction. • Data quality and access controls implemented in practice using Unity Catalog, Microsoft Purview, or equivalent tooling. • Strong SQL, Python, and PySpark, with Git-based CI/CD delivery (Azure DevOps or equivalent). • Excellent written and verbal communication skills across technical and business audiences. Required (held, or achieved after joining within the period shown): • Databricks Certified Data Engineer Associate or Microsoft Certified: Azure Databricks Data Engineer Associate (DP-750) within 6 months • Databricks Certified Data Engineer Professional within 12 months • Preferred (one or more): • Databricks Certified Generative AI Engineer Associate • Microsoft Certified: Fabric Data Engineer Associate (DP-700) • Microsoft Certified: Fabric Analytics Engineer Associate (DP-600) • Microsoft Certified: Azure Data Engineer Associate (DP-203 retired 2025; accepted if held)